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                        数据分析
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                            <b>1.</b>
                        
                        Python数据分析内容
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                            <b>2.</b>
                        
                        python数据分析环境和工具
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                            <b>2.1.</b>
                        
                        Python数据课程 软件和环境安装
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                            <b>2.2.</b>
                        
                        python发行版
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                            <b>2.3.</b>
                        
                        交互式编辑器-JupyterNotebook
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                            <b>2.3.1.</b>
                        
                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                            <b>2.4.1.</b>
                        
                        pip和Virtualenv
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                            <b>2.5.</b>
                        
                        标记语言：Markdown
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                            <b>2.5.1.</b>
                        
                        Markdown语法
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                            <b>2.5.2.</b>
                        
                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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            <span><b>4.</b> 数据分析库的操作</span>
            
            
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                            <b>4.1.</b>
                        
                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                            <b>4.3.</b>
                        
                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
                    </a>
            
            
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        <li class="chapter " data-level="4.4.4" data-path="数据分析库的操作/8Pandas分组聚合1.html">
            
                
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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        <li class="chapter " data-level="4.4.5" data-path="数据分析库的操作/9Pandas分组聚合2.html">
            
                
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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        <li class="chapter " data-level="4.4.6" data-path="数据分析库的操作/10Pandas数据规整-清理.html">
            
                
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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        <li class="chapter " data-level="4.4.7" data-path="数据分析库的操作/11Pandas数据规整-转换.html">
            
                
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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        <li class="chapter " data-level="4.4.7.1" data-path="数据分析库的操作/14Pandas数据规整-转换-离散化和面元划分.html">
            
                
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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        <li class="chapter " data-level="4.4.8" data-path="数据分析库的操作/16Pandas数据规整-合并.html">
            
                
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
                    </a>
            
            
        </li>
    

            </ul>
            
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        <li class="chapter " data-level="4.5" data-path="数据分析库的操作/13Pandas数据规整-重塑和轴向旋转.html">
            
                
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
                    </a>
            
            
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        <li class="chapter " data-level="4.5.1" data-path="数据分析库的操作/13.1透视表和交叉表.html">
            
                
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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        <li class="chapter " data-level="5" data-path="Python可视化/绘图库-Matplotlib.html">
            
                
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                            <b>5.</b>
                        
                        Python可视化
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        <li class="chapter " data-level="5.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见图表.html">
            
                
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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        <li class="chapter " data-level="5.1.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见设置和操作.html">
            
                
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
                    </a>
            
            
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        <li class="chapter " data-level="5.3" data-path="Python可视化/绘图库-Matplotlib/2Matplotlib-图像组件.html">
            
                
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                            <b>5.3.</b>
                        
                        提升：绘图组件
                    </a>
            
            
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        <li class="chapter " data-level="5.4" data-path="Python可视化/绘图库-Matplotlib/3Matplotlib-高级绘图.html">
            
                
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
                    </a>
            
            
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
                    </a>
            
            
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        <li class="chapter " data-level="5.6" data-path="Python可视化/绘图库-Matplotlib/5注意事项.html">
            
                
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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        <li class="chapter " data-level="5.7" data-path="Python可视化/绘图库-Matplotlib/6pylab.html">
            
                
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                            <b>5.7.</b>
                        
                        拓展：pylab
                    </a>
            
            
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        <li class="chapter " data-level="6" data-path="数据分析必备知识点/数据分析必备知识点汇集.html">
            
                
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                            <b>6.</b>
                        
                        数据分析必备知识点
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        <li class="chapter " data-level="7" data-path="数据分析必备知识点/数据分析流程.html">
            
                
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                            <b>7.</b>
                        
                        数据分析流程
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 ---
 ![&#x56FE;&#x7247;&#x63CF;&#x8FF0;](images/bubble.jpg)

`&#xFF01;[&#x56FE;&#x7247;&#x63CF;&#x8FF0;](&#x56FE;&#x7247;&#x8DEF;&#x5F84;)`

####&#x65E0;&#x5E8F;&#x5217;&#x8868;

---
* &#x65E0;&#x5E8F;&#x5217;&#x8868;
    * &#x65E0;&#x5E8F;&#x5217;&#x8868;
        * &#x65E0;&#x5E8F;&#x5217;&#x8868;
            * &#x65E0;&#x5E8F;&#x5217;&#x8868;
                * &#x65E0;&#x5E8F;&#x5217;&#x8868;

####&#x6709;&#x5E8F;&#x5217;&#x8868;

---
1. &#x6709;&#x5E8F;&#x5217;&#x8868;
1. &#x6709;&#x5E8F;&#x5217;&#x8868;
    1. &#x6709;&#x5E8F;&#x5217;&#x8868;
        1. &#x6709;&#x5E8F;&#x5217;&#x8868;
        1. &#x6709;&#x5E8F;&#x5217;&#x8868;

####&#x4EE3;&#x7801;&#x5757;

---
 aaaaaa`&lt;div&gt;bbb&lt;/div&gt;`sgahdj

```python
a = 1
print(a)
``` 

####&#x8868;&#x683C;

---
&#x8868;&#x5934;1 | &#x8868;&#x5934;2 | &#x8868;&#x5934;3
----- | ----- | -----
&#x884C;1&#x5217;1 | &#x884C;1&#x5217;2 | &#x884C;1&#x5217;3
&#x884C;2&#x5217;1 | &#x884C;2&#x5217;2 | &#x884C;2&#x5217;3

---

&#x4F60;&#x597D;&#x6211;&#x662F;*&#x5218;&#x53CC;&#x4E39;*&#xFF0C;&#x4ECA;&#x5E74;\*\*&#x5C81;
</code></pre><h2 id="&#x8BED;&#x6CD5;&#x8BE6;&#x89E3;&#xFF1A;">&#x8BED;&#x6CD5;&#x8BE6;&#x89E3;&#xFF1A;</h2>
<h4 id="&#x6807;&#x9898;-h1h6">&#x6807;&#x9898; (#h1-h6)</h4>
<hr>
<h1 id="&#x4E00;&#x7EA7;&#x6807;&#x9898;">&#x4E00;&#x7EA7;&#x6807;&#x9898;</h1>
<h2 id="&#x4E8C;&#x7EA7;&#x6807;&#x9898;">&#x4E8C;&#x7EA7;&#x6807;&#x9898;</h2>
<h3 id="&#x4E09;&#x7EA7;&#x6807;&#x9898;">&#x4E09;&#x7EA7;&#x6807;&#x9898;</h3>
<h4 id="&#x56DB;&#x7EA7;&#x6807;&#x9898;">&#x56DB;&#x7EA7;&#x6807;&#x9898;</h4>
<h5 id="&#x4E94;&#x7EA7;&#x6807;&#x9898;">&#x4E94;&#x7EA7;&#x6807;&#x9898;</h5>
<h6 id="&#x516D;&#x7EA7;&#x6807;&#x9898;">&#x516D;&#x7EA7;&#x6807;&#x9898;</h6>
<h4 id="&#x6BB5;&#x843D;">&#x6BB5;&#x843D;</h4>
<hr>
<p>&#x6BB5;&#x843D;</p>
<p>&#x4E4B;&#x95F4;&#x7A7A;&#x4E00;&#x884C;</p>
<h4 id="&#x8D85;&#x94FE;&#x63A5;">&#x8D85;&#x94FE;&#x63A5;</h4>
<hr>
<p><code>[&#x767E;&#x5EA6;](http://www.baidu.com/)</code>
<a href="http://www.baidu.com/" target="_blank">&#x767E;&#x5EA6;</a></p>
<h4 id="&#x56FE;&#x7247;">&#x56FE;&#x7247;</h4>
<hr>
<p> <img src="images/bubble.jpg" alt="&#x56FE;&#x7247;&#x63CF;&#x8FF0;"></p>
<p><code>&#xFF01;[&#x56FE;&#x7247;&#x63CF;&#x8FF0;](&#x56FE;&#x7247;&#x8DEF;&#x5F84;)</code></p>
<h4 id="&#x65E0;&#x5E8F;&#x5217;&#x8868;">&#x65E0;&#x5E8F;&#x5217;&#x8868;</h4>
<hr>
<ul>
<li>&#x65E0;&#x5E8F;&#x5217;&#x8868;<ul>
<li>&#x65E0;&#x5E8F;&#x5217;&#x8868;<ul>
<li>&#x65E0;&#x5E8F;&#x5217;&#x8868;<ul>
<li>&#x65E0;&#x5E8F;&#x5217;&#x8868;<ul>
<li>&#x65E0;&#x5E8F;&#x5217;&#x8868;</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<h4 id="&#x6709;&#x5E8F;&#x5217;&#x8868;">&#x6709;&#x5E8F;&#x5217;&#x8868;</h4>
<hr>
<ol>
<li>&#x6709;&#x5E8F;&#x5217;&#x8868;</li>
<li>&#x6709;&#x5E8F;&#x5217;&#x8868;<ol>
<li>&#x6709;&#x5E8F;&#x5217;&#x8868;<ol>
<li>&#x6709;&#x5E8F;&#x5217;&#x8868;</li>
<li>&#x6709;&#x5E8F;&#x5217;&#x8868;</li>
</ol>
</li>
</ol>
</li>
</ol>
<h4 id="&#x4EE3;&#x7801;&#x5757;">&#x4EE3;&#x7801;&#x5757;</h4>
<hr>
<p> aaaaaa<code>&lt;div&gt;bbb&lt;/div&gt;</code>sgahdj</p>
<pre><code class="lang-python">a = <span class="hljs-number">1</span>
print(a)
</code></pre>
<h4 id="&#x8868;&#x683C;">&#x8868;&#x683C;</h4>
<hr>
<table>
<thead>
<tr>
<th>&#x8868;&#x5934;1</th>
<th>&#x8868;&#x5934;2</th>
<th>&#x8868;&#x5934;3</th>
</tr>
</thead>
<tbody>
<tr>
<td>&#x884C;1&#x5217;1</td>
<td>&#x884C;1&#x5217;2</td>
<td>&#x884C;1&#x5217;3</td>
</tr>
<tr>
<td>&#x884C;2&#x5217;1</td>
<td>&#x884C;2&#x5217;2</td>
<td>&#x884C;2&#x5217;3</td>
</tr>
</tbody>
</table>
<hr>
<p>&#x4F60;&#x597D;&#x6211;&#x662F;<em>&#x5218;&#x53CC;&#x4E39;</em>&#xFF0C;&#x4ECA;&#x5E74;**&#x5C81;</p>

                    
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